Splits
| Split | Use | Risk |
|---|---|---|
| Global temporal cut-off | closest to deployment; preferred | test users may be new (evaluate them separately) |
| Leave-last-n per user | common in papers; every user is evaluated | different users' test periods overlap with others' training |
| Random per-user holdout | only without timestamps | leaks future popularity and trends |
| Random over all interactions | never | the worst leakage |
Tune hyperparameters on a validation split cut before the test period, then refit on train + validation.
Accuracy metrics @k
| Metric | Definition (per user, averaged) |
|---|---|
| Precision@k | relevant items in the top k / k |
| Recall@k | relevant items in the top k / all relevant items of the user |
| Hit rate@k | 1 if any relevant item is in the top k |
| MRR | 1 / rank of the first relevant item |
| MAP@k | mean of precision at each relevant position, divided by min(|relevant|, k) |
| NDCG@k | DCG / ideal DCG with gains 2^rel - 1 and log2(rank + 1) discounts |
For a single held-out item per user (leave-last-1), hit rate@k equals recall@k and MRR is common.
Beyond accuracy
| Metric | Definition in rec_eval.py |
|---|---|
| Coverage | distinct recommended items / catalogue (items in train) |
| Novelty | mean of -log2(item train popularity / number of train users) |
| Popularity bias | mean train count of recommended items / mean train count of the catalogue |
| Personalisation | 1 - mean pairwise Jaccard of users' top-k sets |
| Diversity | mean share of distinct categories within a list |
There is a trade-off: popularity scores high on accuracy and low on coverage and novelty. Report both, and decide with the business which matters.
Pitfalls
| Pitfall | Effect |
|---|---|
| Sampled metrics (rank the true item among 100 random negatives) | inconsistent with full ranking; can reverse model order (Krichene and Rendle 2020). Rank against the full catalogue |
| Not filtering seen items | inflated scores for repeat-heavy data, or penalised good models |
| Evaluating only warm users | hides cold-start failure |
| Popularity bias in the test set | models that push bestsellers look better; check item-segment results |
| Tuning on the test set | optimistic results |
| Comparing models at different k | choose k from the product (slots on screen) |
Offline vs online
Offline metrics estimate how well the model predicts what users did under the old system. Online, the new recommendations change what users see and do. Expect gaps. Use offline evaluation to shortlist, A/B tests to decide, and counterfactual estimators (inverse propensity scoring on logged impressions) when logs include the propensities.
anomaly-detection-model-builder
Build and evaluate anomaly detection for tabular, sensor and transaction data with few or no labels: define the anomaly type (point, contextual, collective), choose detectors (robust z-score/MAD and IQR rules, Mahalanobis with robust covariance, k-NN distance, LOF, Isolation Forest, one-class SVM, PCA reconstruction / autoencoders, forecasting residuals for time series), combine them by rank, explain each alert by its deviating features, set thresholds from an alert budget or the score distribution, and evaluate with precision@k, PR-AUC and recall on known incidents, or detector agreement when no labels exist. Includes pure-Python detectors (robust z, Mahalanobis, kNN, Isolation Forest, ensemble) and an evaluation script.
catboost-model-builder
Build CatBoost models that use its strengths correctly: raw categorical columns as cat_features (ordered target statistics, no manual encoding), text_features, Pool construction with NaN-safe categories, ordered boosting and has_time for temporal data, symmetric (oblivious) trees, overfitting detector with eval_set and use_best_model, losses for classification, regression (Quantile, Poisson, Tweedie) and ranking (YetiRank), class weights, monotone constraints, feature importance (PredictionValuesChange, LossFunctionChange) and SHAP values, GPU training and model export. Plans cat/text/numeric columns from a CSV and generates a runnable training script, and checks parameter sets for the conflicts CatBoost rejects (iterations vs n_estimators, Bayesian bootstrap with subsample, multiple class-weight options, depth > 16, use_best_model without eval_set).
categorical-encoding-selector
Choose and implement the right encoding for every categorical column given the model family and the data: one-hot (with infrequent-level grouping and handle_unknown), ordinal with an explicit order, cross-fitted target encoding (scikit-learn TargetEncoder, CatBoost ordered statistics, category_encoders inside CV), frequency/count encoding, feature hashing, native categorical support in LightGBM, HistGradientBoosting, XGBoost and CatBoost, prefix levels for codes (zip3, NAICS-2), and embeddings. Measures cardinality, rare-level share, unseen-level rate on a time-ordered holdout and target signal per column, emits the ColumnTransformer, and quantifies target-encoding leakage (in-sample vs out-of-fold vs holdout AUC).
classification-model-selector
Choose the right classification algorithm for tabular, text or mixed data and prove the choice with a fair bake-off: logistic regression (L2, L1, elastic net), linear SVM, naive Bayes, k-NN, RBF SVM, decision trees, random forest, extra trees, HistGradientBoosting, LightGBM, XGBoost, CatBoost, explainable boosting machines (EBM/GAM), points scorecards and MLPs, matched to data size, width, categorical and high-cardinality share, missingness, class imbalance, text, and constraints such as interpretability, monotonic constraints, calibrated probabilities, latency budgets and tiny deployable models. Ranks candidates with starting hyperparameters and cautions, then generates a bake-off script running every candidate on identical CV folds with per-fold metrics, fit time and prediction latency.
clustering-algorithm-selector
Choose the right clustering algorithm and check that clustering makes sense at all: cluster tendency (Hopkins statistic with a correlated-features caveat, nearest-neighbour isolation), then k-means / MiniBatchKMeans, Gaussian mixtures with BIC, agglomerative (Ward, average, complete, single) with dendrograms, DBSCAN, HDBSCAN, OPTICS, spectral clustering, BIRCH, mean shift, k-prototypes and k-modes for mixed or categorical data, and Gower distance, matched to data size, expected shapes, noise, varying density, known k, soft memberships, hierarchy and streaming. Covers preprocessing (scaling, encoding, dimensionality, distance metrics), validation (internal indices, bootstrap stability, external labels, business profiling) and assigning new points.
collaborative-filtering-implementer
Implement collaborative filtering recommenders that beat popularity: item-based kNN with cosine, Jaccard or asymmetric similarity, shrinkage, neighbourhood size, positive-only or mean-centred rating signals and 'because you liked X' explanations; implicit ALS with confidence weighting, BPR pairwise ranking and the closed-form EASE model in plain NumPy, tuned on a temporal validation split by recall@k and NDCG@k and refit on all data; plus user-kNN, explicit matrix factorisation, the implicit/LightFM libraries, hyperparameter ranges, cold-start fallbacks, scaling with sparse matrices and approximate nearest neighbours. Produces top-k lists in the format of the recommendation evaluator.